Basically in the Kyle Model, a market maker finds the likelihood an asset is ending up at a certain price given that a person is an informed trader. Given this, you update what the final price will be by each successive trade through a kalman filter

Thanks for your suggestion it seems like the link mentioned here is for microstructure-tutorial and not for kalman filter. Can you please share link for Kalman Filter.
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AddDec 15 '12 at 14:22

a Kalman Filter is built into the Kyle-model. Implementing the settings for the kyle model will give you a great example of how some market makers actually trade as well as some intuition of real financial markets using kalman filter
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AndrewDec 17 '12 at 15:01

Some great resources there. I think I have a vague sense of how the particle filter works, but I don't find it very intuitive. That March 2003 talk says that PF is best for multi-modal or skewed pdfs (implying that EKF or UKF might be better otherwise). Any insight if you only want to use a Kalman filter with t distributed errors?
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JohnDec 6 '12 at 15:34

well my point was that a Kalman filter makes the assumption of normally distributed error terms and also that the equation must be linear (linear dynamic system) which is not in agreement with empirical evidence gathered from analyzing financial time series data. I remember there was a youtube lecture video implementing a particle filter on stock time series, estimating highs and lows for a momentum based strategy. A quick search did not get me anything. If I find it later then I will post the link
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Matt WolfDec 6 '12 at 15:48

I read a bit about how to use Particle Filters for on line Bayesian estimation. Don't understand all the math yet, but that might be a good enough reason to use them.
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JohnDec 7 '12 at 22:40

The following paper gives you a step-by-step presentation of how to use the Kalman filter in an application in a pricing model framework for a spot and futures market. Everything is explained using Excel: